def test_resize(self):
""" test resize
"""
img = self.img_data
op_class = 'pil'
img_size = (200, 100)
pil_data = run_ops(
[
ops.DecodeImage(op_class=op_class), ops.ResizeImage(
op_class=op_class, size=img_size)
],
img)
self.assertEqual(type(pil_data), PIL.Image.Image)
pil_data = np.array(pil_data)
op_class = 'opencv'
opencv_data = run_ops(
[
ops.DecodeImage(op_class=op_class), ops.ResizeImage(
op_class=op_class, size=img_size)
],
img)
self.assertEqual(type(opencv_data), np.ndarray)
self.assertEqual(pil_data.shape, opencv_data.shape)
self.assertEqual(pil_data.shape, (100, 200, 3))
def test_randcrop(self):
""" test random crop
"""
img = self.img_data
img_size = (200, 100)
crop_size = (50, 20)
op_class = 'pil'
pil_data = run_ops(
[
ops.DecodeImage(op_class=op_class), ops.ResizeImage(
size=img_size, op_class=op_class), ops.RandCropImage(
crop_size, op_class=op_class)
],
img)
self.assertEqual(type(pil_data), PIL.Image.Image)
pil_data = np.array(pil_data)
op_class = 'opencv'
opencv_data = run_ops(
[
ops.DecodeImage(op_class=op_class), ops.ResizeImage(
size=img_size, op_class=op_class), ops.RandCropImage(
crop_size, op_class=op_class)
],
img)
self.assertEqual(type(opencv_data), np.ndarray)
self.assertEqual(pil_data.shape, opencv_data.shape)
self.assertEqual(pil_data.shape, (crop_size[1], crop_size[0], 3))
def test_decode(self):
""" test decode
"""
img = self.img_data
op_class = 'pil'
pil_data = run_ops([ops.DecodeImage(op_class=op_class)], img)
self.assertEqual(type(pil_data), PIL.JpegImagePlugin.JpegImageFile)
pil_data = np.array(pil_data)
op_class = 'opencv'
opencv_data = run_ops([ops.DecodeImage(op_class=op_class)], img)
self.assertEqual(type(opencv_data), np.ndarray)
self.assertEqual(pil_data.shape, opencv_data.shape)
def get_ops(img_size=224, op_class='pil', normalize=True):
""" a image mapper for training data
"""
img_ops = [ops.DecodeImage(op_class=op_class)]
#if op_class == 'pil':
# img_ops += [ops.RandDistortColor(op_class='pil')] # only exist in pil
img_ops += [ops.RotateImage(10, rand=True, op_class=op_class)]
img_ops += [ops.RandCropImage(img_size, op_class=op_class)]
img_ops += [ops.RandFlipImage(op_class=op_class)]
img_ops += [ops.ToCHWImage(op_class=op_class)]
if normalize:
img_ops += [ops.NormalizeImage(op_class=op_class)]
return img_ops
